• Lead data management for complex, global clinical trials from study setup to database lock • Serve as the main client contact for all data management deliverables • Oversee SAE, PK, and external data reconciliation and database deployment • Manage timelines, budgets, and resource allocation to meet customer expectations • Mentor junior data management staff and promote best practices • Drive process improvements and ensure regulatory and SOP compliance

• Develop and maintain strong relationships with clients, understanding their aftermarket needs and objectives. • Act as the primary point of contact for key accounts, providing exceptional customer service and support. • Conduct regular meetings with clients to review aftermarket performance, address concerns, and identify opportunities for improvement. • Monitor and analyze the performance of data center aftermarket services and solutions provided to clients. • Utilize data analytics and reporting tools to track key performance indicators (KPIs) and service levels. • Identify trends, areas for improvement, and opportunities for optimization to enhance client satisfaction and operational efficiency. • Address and resolve client issues or concerns promptly, coordinating with internal teams to ensure effective solutions. • Manage escalations and work to resolve complex or critical problems that impact client performance or satisfaction. • Develop and implement strategic account plans to align with clients’ business goals and requirements. • Propose and execute initiatives to enhance service delivery, drive innovation, and support clients’ evolving needs. • Prepare and present regular performance reports and reviews to clients, highlighting achievements, challenges, and future plans. • Maintain accurate and up-to-date documentation related to account performance, service levels, and client interactions. • Ensure that services and deliverables are provided in accordance with contractual agreements and service level agreements (SLAs).

Role Description Prominence Advisors is actively seeking a Data Governance Lead to join our team. The Data Governance Lead is a strategic consulting leader responsible for helping healthcare organizations establish, operationalize, and mature enterprise Data Governance capabilities that enable trusted, reliable, and actionable data across the organization. This role partners with executive leaders, business stakeholders, and technical teams to embed governance into organizational processes, modern technology platforms, analytics delivery, AI initiatives, and enterprise operating models. As a trusted advisor, you will lead complex client engagements from strategy through execution, helping organizations establish governance operating models, stewardship programs, decision-making frameworks, and sustainable governance practices that improve organizational effectiveness and support digital transformation. Beyond project execution, you will cultivate long-term client relationships, identify opportunities to expand governance capabilities, and contribute to the continued growth and innovation of Prominence's Data Governance practice. The ideal candidate combines deep expertise in enterprise Data Governance with strong healthcare industry knowledge, exceptional communication skills, and the ability to bridge executive leadership, business operations, analytics teams, and technology organizations. - Lead enterprise Data Governance engagements for healthcare organizations. - Serve as a trusted advisor to executive leadership, business stakeholders, and technical teams. - Facilitate governance councils, steering committees, executive workshops, and strategic planning sessions. - Guide clients in developing governance strategies that align with organizational priorities and measurable business outcomes. - Develop and grow long-term client relationships by identifying opportunities to expand governance capabilities beyond initial project engagements. - Lead the integration of Data Governance across the entire data lifecycle—from platform configuration and data acquisition through data product development, analytics execution, AI initiatives, and ongoing operational management. - Bridge development teams, architects, analysts, and business stakeholders to ensure governance is embedded into enterprise processes rather than operating as a separate function. - Champion governance as an enabler of innovation by integrating governance practices into technology implementation, analytics delivery, and organizational decision-making. Qualifications - 5+ years of experience in Data Governance, Data Management, Analytics, Information Management, Healthcare Operations or related disciplines. - Experience leading enterprise Data Governance initiatives. - Experience consulting with healthcare organizations (provider, payer, or life sciences). - Strong knowledge of enterprise Data Governance and Information Management disciplines including: - Data Governance Frameworks - Data Quality - Metadata Management - Master Data Management (MDM) - Data Stewardship - Data Lineage - Data Catalogs - Business Glossaries - Reference Data Management - Data Lifecycle Management - Experience facilitating executive-level governance councils, steering committees, and strategic workshops. - Experience establishing enterprise governance operating models and organizational adoption strategies. - Strong project, stakeholder, and organizational change management skills. - Exceptional communication, facilitation, and executive presentation skills. - Bachelor's degree or equivalent experience. Requirements - Experience with governance platforms such as Microsoft Purview, Collibra, Alation, Informatica, or Atlan. - Familiarity with modern cloud data platforms including Microsoft Fabric, Azure, AWS, Snowflake, Databricks, or Google Cloud. - Knowledge of healthcare interoperability and standards including Epic, FHIR, HL7, and enterprise healthcare data domains. - Experience supporting Healthcare Operations initiatives, including governance of clinical, operational, quality, financial, and performance data used to improve organizational effectiveness. - Experience implementing AI Governance frameworks or supporting responsible AI initiatives. - Experience leading enterprise data modernization initiatives, cloud migrations, or analytics transformations. - Experience developing enterprise data literacy, governance adoption, or organizational change management programs. - Experience with regulatory frameworks including HIPAA and healthcare privacy requirements. - DAMA CDMP certification or other relevant governance certifications. Benefits - Competitive Salaried and Hybrid Compensation Plans - Health Care Plan (Medical, HSAs, Dental & Vision) - Retirement Plan (401k) - Life Insurance (Basic, Voluntary & AD&D) - Dependent & Health Savings Accounts - Short Term & Long Term Disability - Paid Time Off (Vacation/Sick & Public Holidays) - Training & Development Fund - Work From Home - Charitable Giving to Causes You Believe In

Role Description As a Senior Data Scientist on the Enterprise CX team, you are a versatile problem-solver with a solid foundation in end-to-end data science methods. You excel in extracting actionable insights from data to drive strategic decisions and enhance revenue growth. Your expertise lies in conducting deep-dive analyses, diagnosing metric shifts, and applying practical statistical or machine learning methods to solve complex business problems. You are comfortable self-serving across the data stack when needed, and are eager to work collaboratively with stakeholders to deliver impactful solutions that drive business success. What You'll Be Doing - Cross-functional Collaboration & Communication: - Collaborate with cross-functional stakeholders, developing a deep business understanding and supporting synergy across the organization. - Communicate effectively with non-technical stakeholders. - Partner closely with the Customer Success team to provide data-driven insights and support decision-making processes. - End-to-end Analytics: - Deep-Dive Analysis: - Conduct exploratory data analysis and analytical investigations to diagnose metric shifts and uncover actionable trends in customer behavior. - Applied Modeling: - Develop practical predictive models (e.g., churn or upsell forecasting) that directly inform and optimize Customer Success workflows. - Impact Measurement: - Apply basic causal inference and experimentation methodologies to evaluate the true business impact of Customer Success initiatives and product changes. - Self-Serve Engineering: - Build and modify foundational data pipelines and simple dashboards when needed to unblock analyses, partnering with core Data Engineering and BI teams for scalable infrastructure. - Operational Excellence: - Optimize data workflows and contribute to data quality, stepping in to self-serve data extraction and transformation tasks when necessary. - Contribute to the establishment and maintenance of Key Performance Indicators (KPIs) for customer success, leveraging descriptive and diagnostic analytics to drive actionable insights. - Revenue Growth: - Utilize deep-dive analysis and pragmatic modeling to assist in monitoring renewals and identify leading indicators of risk and opportunity. - Support ongoing analysis of customer retention, churn, and revenue trends, leveraging both foundational analytics and statistical methods to identify opportunities for growth. - Analytical Support and Proactive Insights: - Evaluate business performance to identify the root causes of metric shifts, providing proactive data-driven insights to stakeholders. - Assist in making recommendations to improve business productivity and performance, selecting the right analytical tool—from simple SQL aggregations to statistical modeling—to mitigate risks. - Develop AI/LLM-powered solutions to support CS stakeholders. - Customer Success Collaboration: - Work directly with stakeholders in the Customer Success team to create data stories that lead to customer retention and upsell opportunities. Qualifications - Bachelor’s degree or higher in a related field, with a focus on data science, statistics, or a related quantitative discipline. - 3-5 years of relevant experience in data science, with a demonstrated ability to conduct deep-dive analyses, diagnose metric shifts, and apply pragmatic modeling techniques to drive business impact. - Proficiency in applied statistics and practical machine learning, with knowledge of causal inference, experimentation (A/B testing), forecasting, and regression. - Advanced proficiency in SQL for complex data extraction and manipulation, alongside a working knowledge of data pipelining tools (e.g., dbt, Airflow) to self-serve when necessary. - Proficiency in programming languages such as Python for data analysis, automation, and modeling. - Working knowledge of Business Intelligence tools (e.g., Tableau, Sigma), with a strong understanding of best practices for dashboarding and data visualization to communicate insights. - Hands-on experience designing and deploying AI/LLM-based solutions. - Strong communication skills, with the ability to convey complex concepts clearly and effectively to stakeholders. - Strong organizational skills, with the ability to manage multiple projects and deadlines effectively. - A tech-curious mindset with a willingness to learn new technologies and methodologies to stay at the forefront of data science innovation. Compensation - US Zone 3 - 4: $132,000 – $166,000 USD - The range(s) listed above is the expected annual base salary for this role, subject to change. - Salary is just one component of Coursera’s total rewards package. All regular employees are also eligible for a bonus program and equity in the form of RSU’s. - A number of factors are taken into account when determining pay, which includes: job level, location, training/education, business need, skill set and internal equity.